Computer-based modeling or simulation is well known in the art. Such simulations begin with the development of a model of a system that one wishes to test. Most often, the model comprises mathematical equations describing relationships between one or more input (independent) variables and one or more output (dependent) variables. By selecting specific values for the input variables, corresponding output values may be calculated for the output variables. In this manner, one can determine how the system, to the extent that it is accurately represented by the model, will respond to various situations represented by the input values. Note that, as used herein, a “system” may comprise virtually anything that can be represented by an appropriately constructed mathematical model, e.g., business processes, chemical reactions, financial transactions, etc.
One particularly powerful technique for use with simulations is the so-called Monte Carlo analysis technique. In Monte Carlo simulations, a range of plausible input values is designated for each input variable. Likewise, a distribution for each input variable (i.e., a probability distribution function) is also designated. Thereafter, the Monte Carlo simulation generates random inputs for each input variable based on the designated range of values and distributions for the corresponding variables. The random input values are then used to calculate corresponding output values that are thereafter saved, whereas the input values are thrown away. This process is repeated many times, typically numbering in the hundreds or thousands of repetitions, and is used to create statistically meaningful distributions of one or more of the output variables. In this manner, the analyst performing the Monte Carlo simulation (typically the designer of the simulation model) can develop insight into how the model will perform under certain sets of assumed input conditions.
While Monte Carlo simulation techniques are very valuable, beneficial use of the such techniques typically requires intimate knowledge of the underlying simulation model. Furthermore, to the extent that the output distributions are an aggregate of the multitude of tested scenarios, current Monte Carlo simulation tools provided only a limited opportunity to interact with, and therefore develop a true understanding of, the simulation model. Therefore, it would be advantageous to provide a technique for an improved simulation tool based on Monte Carlo analysis that additionally allows a user of the simulation tool to interact with the simulation model results in a relatively simple and intuitive manner.